Tim Baldwin is a Professor in the Department of Computing and Information
Systems, The University of Melbourne, and an Australian Research Council
Future Fellow. He has previously held visiting positions at Cambridge
University, University of Washington, University of Tokyo, Saarland
University, NTT Communication Science Laboratories, and National Institute of
Informatics. His research interests include text mining of social media,
computational lexical semantics, information extraction and web mining, with a
particular interest in the interface between computational and theoretical
linguistics. Current projects include web user forum mining, monitoring and
text mining of Twitter, and text analytics for the creative industries.

Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University
of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of
Technology in 1998 and 2001, respectively. Prior to joining The University of
Melbourne in 2004, he was a Senior Research Engineer at the Center for the
Study of Language and Information, Stanford University (2001-2004).

In this talk, I will present recent work on representation learning for NLP,
focusing on the question of what it buys us in terms of improved "robustness".
I will explore this in three contexts: (1) open-class lexical relation
classification, where we explore the general utility of vector differences
over word embeddings to capture the relation between ordered word pairs; (2)
sequence tagging (POS tagging, chunk parsing, named entity recognition and
multiword expression identification) under varying conditions of
lexical/domain (mis)match; and (3) cross-domain named entity recognition under
varying conditions of class label mismatch.